Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

2021/09/17 by Pedro J. Freire, Yevhenii Osadchuk, Freire, Pedro J. +13
Computer Science · Engineering · #Advanced Photonic Communication Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Optical Network Technologies #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2109.08711

openalex publication_date 2021/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of several neural network architectures, presenting the results for TWC and SSMF set-ups.

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